agentlib_flexquant.modules package

Submodules

agentlib_flexquant.modules.baseline_mpc module

class agentlib_flexquant.modules.baseline_mpc.FlexibilityBaselineMINLPMPC(config: dict, agent: Agent)[source]

Bases: MINLPMPC

pre_computation_hook()[source]: This method is called in every computation step before the optimization starts. Overwrite this method in a derived subclass if you want to take some actions each time before the optimal control problem is solved.

class agentlib_flexquant.modules.baseline_mpc.FlexibilityBaselineMPC(config: dict, agent: Agent)[source]

Bases: MPC

pre_computation_hook()[source]: This method is called in every computation step before the optimization starts. Overwrite this method in a derived subclass if you want to take some actions each time before the optimal control problem is solved.

agentlib_flexquant.modules.flexibility_indicator module

class agentlib_flexquant.modules.flexibility_indicator.FlexibilityIndicatorModule(*args, **kwargs)[source]

Bases: BaseModule

calc_and_send_offer()[source]: Calculate the flexibility KPIs for current predictions, send the flex offer and set the outputs, write and save the results.

callback(inp, name)[source]

check_power_end_deviation(tol: float)[source]: calculates the deviation of the final value of the power profiles and warn the user if it exceeds the tolerance

cleanup_results()[source]

Deletes all files this module created.

Override this method, if your module creates e.g. results files etc.

data: FlexibilityData

get_results() → DataFrame | None[source]: Opens results file of flexibility_indicator.py results_file defined in __init__

process()[source]: This abstract method must be implemented in order to sync the module with the other processes of the agent and the whole MAS.

register_callbacks()[source]

send_flex_offer(name, base_power_profile: Series, pos_diff_profile: Series, pos_price: float, neg_diff_profile: Series, neg_price: float, timestamp: float = None)[source]

Send a flex offer as an agent Variable. The first offer is dismissed, since the different MPCs need one time step to fully initialize.

Inputs:

name: name of the agent variable indicator_data: the indicator data object timestamp: the time offer was generated

write_results(df, ts, n)[source]

Write every data of variables in self.var_list in an DataFrame DataFrame will be updated every time step

Parameters:

df – DataFrame which is initialised as an empty DataFrame with columns according to self.var_list
ts – time step
n – number of time steps during prediction horizon

Returns:

DataFrame with results of every variable in self.var_list

pydantic model agentlib_flexquant.modules.flexibility_indicator.FlexibilityIndicatorModuleConfig[source]

Bases: BaseModuleConfig

Config:

arbitrary_types_allowed: bool = True
validate_assignment: bool = True
extra: str = forbid
frozen: bool = True

Fields:

calculate_costs (agentlib_flexquant.modules.flexibility_indicator.InputsForCalculateFlexCosts)
correct_costs (agentlib_flexquant.modules.flexibility_indicator.InputsForCorrectFlexCosts)
inputs (List[agentlib.core.datamodels.AgentVariable])
outputs (List[agentlib.core.datamodels.AgentVariable])
parameters (List[agentlib.core.datamodels.AgentVariable])
power_unit (str)
price_variable (str)
results_file (pathlib.Path | None)
save_results (bool | None)
shared_variable_fields (List[str])

Validators:

check_results_file_extension » all fields
check_valid_fields » shared_variable_fields

field calculate_costs: InputsForCalculateFlexCosts = InputsForCalculateFlexCosts(use_constant_electricity_price=False, calculate_flex_costs=True, const_electricity_price=nan)

Validated by:

check_results_file_extension

field correct_costs: InputsForCorrectFlexCosts = InputsForCorrectFlexCosts(enable_energy_costs_correction=False, absolute_power_deviation_tolerance=0.1, stored_energy_variable='E_stored')

Validated by:

check_results_file_extension

field inputs: List[AgentVariable] = [AgentVariable(name='_P_el_base', type='pd.Series', timestamp=None, unit='W', description='The power input to the system', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_P_el_base', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='_P_el_neg', type='pd.Series', timestamp=None, unit='W', description='The power input to the system', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_P_el_neg', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='_P_el_pos', type='pd.Series', timestamp=None, unit='W', description='The power input to the system', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_P_el_pos', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='_E_stored_base', type='pd.Series', timestamp=None, unit='kWh', description='Energy stored in the system w.r.t. 0K', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_E_stored_base', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='_E_stored_neg', type='pd.Series', timestamp=None, unit='kWh', description='Energy stored in the system w.r.t. 0K', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_E_stored_neg', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='_E_stored_pos', type='pd.Series', timestamp=None, unit='kWh', description='Energy stored in the system w.r.t. 0K', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_E_stored_pos', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

Validated by:

check_results_file_extension

field outputs: List[AgentVariable] = [AgentVariable(name='FlexibilityOffer', type='FlexOffer', timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='FlexibilityOffer', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_full', type='pd.Series', timestamp=None, unit='W', description='Negative power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_full', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_full', type='pd.Series', timestamp=None, unit='W', description='Positive power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_full', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_offer', type='pd.Series', timestamp=None, unit='W', description='Negative power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_offer', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_offer', type='pd.Series', timestamp=None, unit='W', description='Positive power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_offer', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_offer_min', type='float', timestamp=None, unit='W', description='Minimum of negative power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_offer_min', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_offer_min', type='float', timestamp=None, unit='W', description='Minimum of positive power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_offer_min', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_offer_max', type='float', timestamp=None, unit='W', description='Maximum of negative power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_offer_max', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_offer_max', type='float', timestamp=None, unit='W', description='Maximum of positive power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_offer_max', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_offer_avg', type='float', timestamp=None, unit='W', description='Average of negative power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_offer_avg', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_offer_avg', type='float', timestamp=None, unit='W', description='Average of positive power flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_offer_avg', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_power_flex_within_boundary', type='bool', timestamp=None, unit='-', description='Variable indicating whether the baseline power and flex power align at the horizon end', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_power_flex_within_boundary', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_power_flex_within_boundary', type='bool', timestamp=None, unit='-', description='Variable indicating whether the baseline power and flex power align at the horizon end', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_power_flex_within_boundary', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_energy_flex', type='float', timestamp=None, unit='kWh', description='Negative energy flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_energy_flex', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_energy_flex', type='float', timestamp=None, unit='kWh', description='Positive energy flexibility', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_energy_flex', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_costs', type='float', timestamp=None, unit='ct', description='Saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_costs', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_costs', type='float', timestamp=None, unit='ct', description='Saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_costs', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_corrected_costs', type='float', timestamp=None, unit='ct', description='Corrected saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_corrected_costs', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_corrected_costs', type='float', timestamp=None, unit='ct', description='Corrected saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_corrected_costs', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_costs_rel', type='float', timestamp=None, unit='ct/kWh', description='Saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_costs_rel', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_costs_rel', type='float', timestamp=None, unit='ct/kWh', description='Saved costs due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_costs_rel', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='negative_corrected_costs_rel', type='float', timestamp=None, unit='ct/kWh', description='Corrected saved costs per energy due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='negative_corrected_costs_rel', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='positive_corrected_costs_rel', type='float', timestamp=None, unit='ct/kWh', description='Corrected saved costs per energy due to baseline', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='positive_corrected_costs_rel', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

Validated by:

check_results_file_extension

field parameters: List[AgentVariable] = [AgentVariable(name='prep_time', type=None, timestamp=None, unit='s', description='Preparation time', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='prep_time', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='market_time', type=None, timestamp=None, unit='s', description='Market time', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='market_time', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='flex_event_duration', type=None, timestamp=None, unit='s', description='time to switch objective', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='flex_event_duration', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='time_step', type=None, timestamp=None, unit='s', description='timestep of the mpc solution', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='time_step', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='prediction_horizon', type=None, timestamp=None, unit='-', description='prediction horizon of the mpc solution', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='prediction_horizon', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

Validated by:

check_results_file_extension

field power_unit: str = 'kW'

Unit of the power variable

Validated by:

check_results_file_extension

field price_variable: str = 'c_pel'

Name of the price variable sent by a predictor

Validated by:

check_results_file_extension

field results_file: Path | None = PosixPath('flexibility_indicator.csv')

User specified results file name

Validated by:

check_results_file_extension

field save_results: bool | None = True

Validated by:

check_results_file_extension

field shared_variable_fields: List[str] = ['outputs']

Validated by:

check_results_file_extension
check_valid_fields

validator check_results_file_extension » all fields[source]

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

pydantic model agentlib_flexquant.modules.flexibility_indicator.InputsForCalculateFlexCosts[source]

Bases: BaseModel

Fields:

calculate_flex_costs (bool)
const_electricity_price (float)
use_constant_electricity_price (bool)

Validators:

validate_constant_price » all fields

field calculate_flex_costs: bool = True

Calculate the flexibility cost

Validated by:

validate_constant_price

field const_electricity_price: float = nan

constant electricity price in ct/kWh

Validated by:

validate_constant_price

field use_constant_electricity_price: bool = False

Use constant electricity price

Validated by:

validate_constant_price

validator validate_constant_price » all fields[source]

pydantic model agentlib_flexquant.modules.flexibility_indicator.InputsForCorrectFlexCosts[source]

Bases: BaseModel

Fields:

absolute_power_deviation_tolerance (float)
enable_energy_costs_correction (bool)
stored_energy_variable (str)

field absolute_power_deviation_tolerance: float = 0.1: Absolute tolerance in kW within which no warning is thrown

field enable_energy_costs_correction: bool = False: Variable determining whether to correct the costs of the flexible energyDefine the variable for stored electrical energy in the base MPC model and config as output if the correction of costs is enabled

field stored_energy_variable: str = 'E_stored': Name of the variable representing the stored electrical energy in the baseline config

agentlib_flexquant.modules.flexibility_market module

class agentlib_flexquant.modules.flexibility_market.FlexibilityMarketModule(*, config: dict, agent: Agent)[source]

Bases: BaseModule

Class to emulate flexibility market. Receives flex offers and accepts these.

cleanup_results()[source]

Deletes all files this module created.

Override this method, if your module creates e.g. results files etc.

custom_flexibility_callback(inp, name)[source]: Placeholder for a custom flexibility callback

df: DataFrame = None

dummy_callback(inp, name)[source]: Dummy function, that is included, when market type is not specified

end: int | float = 0

get_results() → DataFrame | None[source]: Opens results file of flexibilityindicators.py results_file defined in __init__

process()[source]: This abstract method must be implemented in order to sync the module with the other processes of the agent and the whole MAS.

random_flexibility_callback(inp, name)[source]

When a flexibility offer is sent this function is called.

The offer is accepted randomly. The factor self.offer_acceptance_rate determines the
random factor for offer acceptance. self.pos_neg_rate is the random factor for the direction of the flexibility. A higher rate means that more positive offers will be accepted.

Constraints:
cooldown: during $cooldown steps after a flexibility event no offer is accepted minimum_average_flex: min amount of flexibility to be accepted, to account for the model error

register_callbacks()[source]

set_random_seed(random_seed)[source]: set the random seed for reproducability

single_flexibility_callback(inp, name)[source]: Callback to activate a single, predefined flexibility offer.

write_results(offer)[source]

pydantic model agentlib_flexquant.modules.flexibility_market.FlexibilityMarketModuleConfig[source]

Bases: BaseModuleConfig

Config:

arbitrary_types_allowed: bool = True
validate_assignment: bool = True
extra: str = forbid
frozen: bool = True

Fields:

inputs (List[agentlib.core.datamodels.AgentVariable])
market_specs (agentlib_flexquant.data_structures.market.MarketSpecifications)
outputs (List[agentlib.core.datamodels.AgentVariable])
results_file (pathlib.Path | None)
save_results (bool | None)
shared_variable_fields (List[str])

Validators:

check_results_file_extension » all fields
check_valid_fields » shared_variable_fields

field inputs: List[AgentVariable] = [AgentVariable(name='FlexibilityOffer', type=None, timestamp=None, unit='Not defined', description='Not defined', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='FlexibilityOffer', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

Validated by:

check_results_file_extension

field market_specs: MarketSpecifications [Required]

Validated by:

check_results_file_extension

field outputs: List[AgentVariable] = [AgentVariable(name='_P_external', type=None, timestamp=None, unit='Not defined', description='External Power IO', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='_P_external', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='rel_start', type=None, timestamp=None, unit='Not defined', description='relative start time of the flexibility event', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='rel_start', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='rel_end', type=None, timestamp=None, unit='Not defined', description='relative end time of the flexibility event', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='rel_end', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='in_provision', type=None, timestamp=None, unit='Not defined', description='Set if the system is in provision', ub=inf, lb=-inf, clip=False, allowed_values=[], value=False, alias='in_provision', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

Validated by:

check_results_file_extension

field results_file: Path | None = PosixPath('flexibility_market.csv')

User specified results file name

Validated by:

check_results_file_extension

field save_results: bool | None = True

Validated by:

check_results_file_extension

field shared_variable_fields: List[str] = ['outputs']

Validated by:

check_results_file_extension
check_valid_fields

validator check_results_file_extension » all fields[source]

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

agentlib_flexquant.modules.shadow_mpc module

class agentlib_flexquant.modules.shadow_mpc.FlexibilityShadowMINLPMPC(*args, **kwargs)[source]

Bases: MINLPMPC

calc_flex_callback(inp, name)[source]: set the control trajectories before calculating the flexibility offer. self.model should account for flexibility in its cost function

process()[source]: This abstract method must be implemented in order to sync the module with the other processes of the agent and the whole MAS.

register_callbacks()[source]: Registers the init_optimization callback to all parameters which cannot be changed without recreating the optimization problem.

class agentlib_flexquant.modules.shadow_mpc.FlexibilityShadowMPC(*args, **kwargs)[source]

Bases: MPC

calc_flex_callback(inp, name)[source]: set the control trajectories before calculating the flexibility offer. self.model should account for flexibility in its cost function

process()[source]: This abstract method must be implemented in order to sync the module with the other processes of the agent and the whole MAS.

register_callbacks()[source]: Registers the init_optimization callback to all parameters which cannot be changed without recreating the optimization problem.